Beyond the Promise of AI: Putting patients at the heart of healthcare in Nigeria

By PAVEL URSU For a pregnant woman in a rural settlement in Kuje, Abuja, accessing specialist care may mean hours of travel, considerable expense, and uncertainty. For a child in a hard-to-reach community in Borno State, pneumonia may go undetected because a health worker lacks the necessary diagnostic tools. In a riverine community in Bayelsa State, a person with tuberculosis (TB) may face delays in diagnosis because radiology services are difficult to access.

These scenarios reflect a fundamental challenge facing Nigeria’s healthcare system: those who need timely medical attention most often face the greatest barriers to receiving it. Again, consider a scenario in which a frontline health worker is equipped with an artificial intelligence (AI)-powered tool that facilitates early identification of complications, interpretation of medical images, or recognition of patients requiring urgent referrals.

As African countries explore AI-enabled healthcare, a critical question emerges: is technology being adopted solely due to its availability, or because it demonstrably improves health outcomes? To be sure, the value of AI lies not in its sophistication, but in its ability to improve health outcomes, expand access to appropriate care and reduce inequalities.

AI offers opportunities to strengthen healthcare delivery, particularly where specialists, diagnostic equipment and other resources are limited. When properly validated and implemented, it can help health workers identify risks, interpret clinical information and make informed decisions. Tuberculosis screening illustrates this potential. AI-powered computer-aided detection (CAD) tools analyze chest X-rays to identify abnormalities suggestive of tuberculosis, helping health workers prioritize individuals for further testing.

For eligible populations aged 15 years and older, these tools can improve screening and triage, particularly in settings with limited access to radiologists. However, screening must be followed by confirmatory testing, clinical assessment and timely linkage to treatment. Nigeria offers encouraging evidence of this potential. A study involving 869 participants across 17 communities in the Federal Capital Territory evaluated an AI-enabled automated microscope for detecting Schistosoma haematobium ) eggs in urine, an indicator of urinary schistosomiasis.

Compared with conventional microscopy, the tool demonstrated approximately 89 per cent sensitivity and 99 per cent specificity. Although promising, these findings require further validation in routine clinical settings. Affordability, reliability, quality assurance and scalability will determine whether such innovations can deliver sustained benefits.

Put differently, success should be measured not only by AI’s ability to detect disease, but by whether patients receive appropriate care as a result. For many Nigerians, distance, transport costs, language barriers and shortages of health workers limit access to essential services. Voice-based AI could support frontline workers and improve access to reliable health information, provided these tools accommodate local languages, cultural contexts and limited internet connectivity.

Community-informed design and validation are essential. AI should enhance, rather than replace, the trust between patients and health workers. Similarly, AI-enabled surveillance could help identify communities missed by routine immunization and improve polio response planning. However, data alone cannot vaccinate a child. Health workers, available vaccines and community trust remain indispensable.

AI’s contribution must therefore be measured by how effectively it strengthens existing health systems and extends their reach. Nigeria’s realities must guide the development and implementation of AI in healthcare. Unreliable electricity limited rural connectivity, fragmented health information systems, high costs, and shortages of technical expertise can undermine promising innovations.

For a rural health facility, the practical questions are straightforward: Can the tool function during power interruptions? Can health workers use it with limited connectivity? Is technical support available when it fails? Will the service remain operational beyond the pilot phase? Implementation costs must extend beyond purchasing technology to include electricity, connectivity, system integration, staff training, maintenance, cybersecurity and regulatory compliance.

Solutions dependent on short-term donor funding risk becoming unsustainable once external support ends. Interoperability is equally important. Nigeria’s digital health systems must securely exchange relevant information rather than operate in isolation. Integrated infrastructure can reduce duplication, improve continuity of care and make better use of limited resources.

Local ownership is also critical. Nigeria’s researchers, universities, health workers, regulators, government institutions and technology developers must actively